How do you handle missing values in a dataset before building a machine learning model?

Missing values should be handled based on the type of data and the reason they are missing. For numerical data, you can use the mean or median, while categorical data can often be filled with the most frequent value or a separate “Unknown” category. If a column has too many missing values, removing it may be an option. The key is to choose an approach that fits the dataset and avoids introducing bias into the model.

Classification and regression are both supervised machine learning techniques, but they are used for different types of predictions.

Classification is used when the output is a category or class. For example, an email can be classified as "Spam" or "Not Spam."

Regression is used when the output is a continuous numerical value. For example, we can use regression to predict the price of a house based on its location, size, number of rooms, and other features.

So, simply put: classification predicts a category, while regression predicts a numerical value.

Missing values should be handled based on the type of data and the reason they are missing. For numerical data, you can use the mean or median, while categorical data can often be filled with the most frequent value or a separate “Unknown” category. If a column has too many missing values, removing it may be an option. The key is to choose an approach that fits the dataset and avoids introducing bias into the model.